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Dead reckoning, estimated position with current, and computed set and drift

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# Dead reckoning, estimated position with current, and computed set and drift `input.json` (synthetic, not for navigation): `start` [lat, lon] in decimal degrees, `legs` (each `course` degrees true, `speed_kn` through the water, `minutes`), a forecast `current` (`set` = direction it flows TOWARD, degrees true; `drift_kn`), and a GPS `fix` [lat, lon] at the end of the last leg. Write `solve.py` (Python 3 standard library only); `python solve.py input.json answer.json` writes `answer.json` and `track.svg`. Fresh inputs are used too. ## Rules (plane sailing, 1 minute of latitude = 1 nm) 1. A move of n nm north and e nm east from (lat, lon): dlat = n / 60; mid = lat + dlat / 2; new lat = lat + dlat; new lon = lon + e / (60 * cos(mid)). 2. **DR**: per leg, distance = speed * hours along the course. **EP**: per leg, add the current vector (drift * hours toward set) to the leg's water vector, then move once. 3. Difference from point A to point B: north = (latB - latA) * 60, east = (lonB - lonA) * 60 * cos((latA + latB) / 2). 4. **Set/drift**: vector from the final DR position to the fix; set = atan2(east, north) in degrees mod 360; drift = length / total hours. `ep_error_nm` = distance from the final EP to the fix. **CMG/SMG**: same from `start` to the fix. 5. Round positions to 5 decimals, hours to 4, angles to 1, speeds and distances to 3. `dr` and `ep` list the start and the position after every leg. ## Visual output: track.svg Static, self-contained SVG (root `<svg>`, no scripts, images, links or external assets; renders headless) with `<polyline id="dr-track">`, `<polyline id="ep-track">` and `<circle id="fix">`. The drawing itself is not graded. ## answer.json `{"dr": [[lat, lon], ...], "ep": [...], "hours", "set_deg", "drift_kn", "ep_error_nm", "cmg_deg", "smg_kn"}` ## Scoring Hidden checks (partial credit; all must pass): `dr_positions`, `ep_positions`, `set_and_drift`, `course_speed_made_good`, `track_svg_static` (track.svg exists, follows the static rules, has the ids/tags above), `fresh_run_1`, `fresh_run_2`. Positions within 2e-5 degrees, angles within 0.11, speeds and distances within 0.002. 60-second limit per run.

Mid-latitude longitude scaling per leg, current applied per leg as a vector, 'set' as the direction the current flows toward, angle wrap, and positions checked at five decimals at high latitudes. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Benchmark code for comparing AI models, not navigation or safety software.

Success criteria: 1. dr_positions 2. ep_positions 3. set_and_drift 4. course_speed_made_good 5. track_svg_static 6. fresh_run_1 7. fresh_run_2

Best model for this task so far: DeepSeek V4.1 Flash (Venice) (thinking on) (success, $0.0073, 23s).

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All runs (9)

100% pass rate across 9 eligible runs: 9 pass, 0 partial, 0 fail. Partial and failed runs are listed below with everything else.ⓘ

Runs for this use case
ModelCostTimeStep-insⓘAutonomyOutcomeTrustⓘDateCompare
QQwen3 Coder 480B Turbo (Venice)thinking on$0.026m0Fully autonomouspassTrust 80%2026-10-27
DDeepSeek V4.1 Flash (Venice)thinking on$0.007323s0Fully autonomouspassTrust 80%2026-10-27
ZGLM 4.7 (Venice)thinking on$0.056m0Fully autonomouspassTrust 80%2026-10-23
GGemini 4 Argonmedium · thinking on$0.142m0Fully autonomouspassTrust 80%2026-10-22
GGemini 3.8 Flashmedium · thinking on$0.293m0Fully autonomouspassTrust 80%2026-10-22
OGPT-6.1 Soldefault · thinking on$0.041m0Fully autonomouspassTrust 80%2026-10-22
AClaude Haiku 5.5default · thinking on$0.007429s0Fully autonomouspassTrust 80%2026-10-20
AClaude Opus 5.5default · thinking on$0.1735s0Fully autonomouspassTrust 80%2026-10-20
AClaude Sonnet 5.5default · thinking on$0.0622s0Fully autonomouspassTrust 80%2026-10-20

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